biostudies-literatureUnknownJiang JNICHD NIH HHSNCI NIH HHSNIGMS NIH HHSNIH HHSGene regulatory networks within cells modulate the expression of the genome in response to signals and changing environmental conditions. Reconstructions of gene regulatory networks can reveal the information processing and control principles used by cells to maintain homeostasis and execute cell-state transitions. Here, we introduce a computational framework, D-SPIN, that generates quantitative models of gene regulatory networks from single-cell mRNA-seq datasets collected across thousands of distinct perturbation conditions. D-SPIN constructs probabilistic models of regulatory interactions between genes or gene-expression programs to fit the cell state distributions under different perturbations. Using large Perturb-seq and drug-response datasets, we demonstrate that D-SPIN models revealbioRxiv : the preprint server for biology2023.04.19.537364https://www.ebi.ac.uk/biostudies/studies/S-EPMC10153191biostudies-literatureD-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response.PMC10153191U01 CA244109DP5 OD012194R01 HD100039R01 GM135337S10 OD028511R01 GM144367R01 GM150125Sivak DAJiang JPark JHVielmetter JTsou TThomson MMcGinnis CSGartner ZJGong YStrazhnik IMHanna JKhazaei TChen SZhu QChow EDfalseD-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response.Gene regulatory networks within cells modulate the expression of the genome in response to signals and changing environmental conditions. Reconstructions of gene regulatory networks can reveal the information processing and control principles used by cells to maintain homeostasis and execute cell-state transitions. Here, we introduce a computational framework, D-SPIN, that generates quantitative models of gene regulatory networks from single-cell mRNA-seq datasets collected across thousands of distinct perturbation conditions. D-SPIN constructs probabilistic models of regulatory interactions between genes or gene-expression programs to fit the cell state distributions under different perturbations. Using large Perturb-seq and drug-response datasets, we demonstrate that D-SPIN models reveal2025-01-01T00:00:00Z2025 May2026-04-08T14:17:06.739Z2025-04-19T15:04:36.913ZS-EPMC101531913713180310.1101/2023.04.19.537364